This invention relates to a digital imaging system wherein noise and other non-image data associated with the input devices and media is propagated through a series of imaging transforms. Particularly, this invention relates to the methods for propagating the noise and other non-image data through an image processing chain.
Many image processing operations require non-image information in order to properly adapt their parameters to the expected conditions. Non-image information refers to data that is carried along with each image and provides information about the capture conditions, devices, expected noise statistics, etc. Non-image data will herein be also referred to as metadata. An example of this type of image processing operation is a noise reduction algorithm that uses a table of noise root-mean-square (rms) values for each color record for every signal level. The rms value of a nominally uniform image area is a measure of the pixel-to-pixel variation, and may be computed from a set of N values, as:                               σ          x                =                                                                              ∑                  N                                                  i                  =                  1                                            ⁢                                                (                                                            x                      i                                        -                                          μ                      x                                                        )                                2                                                    N              -              1                                                          Eq.  (1)            
where the sample mean is             μ      x        =                                        ∑            N                                i            =            1                          ⁢                  x          i                    N        ,
xi is the pixel value of the ith-location and "sgr"x is the rms pixel-to-pixel variation. Thus if the rms value is computed for uniform areas of various signal levels, the set of these values can be seen to characterize the amplitude of image noise in actual scenes acquired using the same image source, as a function of image signal.
The rms statistics could be used to adaptively discern texture from noise in localized regions of an image. Another example is image dependent sharpening where a measure of sharpness loss could be used along with rms statistics to adaptively change the level of sharpening in the image. During the processing of image information in a multistage imaging system, however, the metadata, including noise statistics, may be changed by every operation or transformation applied to the signal. If this transformation of metadata is not taken into account, then subsequent adaptive operations will not operate as intended and system performance, usually in terms of image quality, will suffer.
One way to account for the transformation of the non-image information is to estimate it directly at every step in the imaging system. For instance, U.S. Pat. No. 5,641,596 issued Jun. 24, 1997 to Gray et al., entitled xe2x80x9cAdjusting Film Grain Properties in Digital Imagesxe2x80x9d, discloses a method for measuring image noise statistics based on the scanning of several uniform step patches that are not usually present in actual scenes. This estimation step could be used in imaging systems where the image processing operations are deterministic and there exists flexibility to process the set of uniform patches. However, it does not account for adaptive transformation of the noise statistics after the estimation step.
Another approach is U.S. patent application Ser. No. 08/822,722 filed Mar. 24, 1997, by Snyder et al., now allowed as of Feb. 1, 1999, which teaches how to automatically create tables consisting of noise rms values as a function of signal level from real images. Again, the method does not teach how to transform the noise statistics based on image processing operations thereafter.
Image noise propagation through several common imaging operations is addressed in the article by Peter D. Bums and Roy S. Berns, in Color Research and Application, entitled xe2x80x9cError Propagation Analysis in Color Measurement and Imagingxe2x80x9d. The analysis was not, however, applied to the transformations of noise statistics in image processing systems for use by adaptive algorithms.
Similarly, it is very useful to provide data associated with the image capture devices or capture conditions, that could be smartly used by image processing algorithms to deliver enhanced image quality. U.S. Pat. No. 5,461,440 issued Oct. 24, 1995 to Toyoda et al., entitled xe2x80x9cPhotographing Image Correction System,xe2x80x9d discloses a photographic image correction system to correct an image on the basis of degradation information inherent in a camera body or lens. The degradation information is inferred from the information of the camera and lens that is recorded in the film. This method does not teach, however, how to adjust the degradation information after subsequent image processing.
U.S. Pat. No. 5,694,484 issued Dec. 2, 1997 to Cottrell et al., entitled xe2x80x9cSystem and Method for Automatically Processing Image Data to Provide Images of Optimal Perceptual Qualityxe2x80x9d, teaches a method for selecting proper image transforms to a given set of metadata. However, this method does not teach the adjustment of the metadata specific to the image transform.
The prior art presented acknowledges the usefulness of noise statistics and other metadata in image processing applications. However, no prior art addresses the propagation of such data in an imaging system after its collection.
The object of the invention is to provide a method for modification of noise and other metadata along an image processing chain to improve the utility of data in subsequent operations and thereby improve the system performance and the image quality. It is necessary that the metadata be transformed after each image processing operation, so that it reflects the changes made to the image. The object is accomplished by:
a) providing metadata corresponding to characteristics of a specific digital image;
b) generating a metadata transformation related to at least one specific image transformation; and
c) modifying the metadata according to the metadata transformation.
A further object of the present invention is to provide an alternative method for modification of metadata in an image processing chain to improve the output of the captured image data after processing. The metadata should be applied only on demand. The object is accomplished by a method that comprises the steps of:
a) providing metadata corresponding to characteristics of a specific digital image;
b) generating an image target whose characteristics are described by the provided metadata;
c) providing an image transformation;
d) processing the image target through the image transformation applied to the digital image; and
e) calculating the modified metadata from the processed image target.
It is an object of the invention to provide an image reproduction system for modifying metadata during several steps of image processing. This improves image data output when compared to the original digital image data. The object is accomplished by a system that comprises:
a) an imaging capture device for providing digital image data;
b) memory device for storing noise and other metadata associated with the digital image data;
c) an image processing chain for carrying out image transformations; and
d) a metadata processing chain being connected to the image processing chain in cases where the image transformations are metadata sensitive.
A further object of the invention is to provide an image reproduction system for modifying noise and other metadata during several steps of image processing. The metadata should only be applied on demand. The object is accomplished by a system that comprises:
a) an imaging capture device for providing digital image data;
b) memory device for storing noise and other metadata associated with the digital image data;
c) a first processing chain for carrying out image transformations;
d) a second processing chain for carrying out transformations on a target image; and
e) a target image evaluator for determining metadata from the target image in the second processing chain and thereby providing input to a metadata sensitive image transformation in the first processing chain.
No prior art provides the details of metadata propagation in an imaging system, with application to adaptive algorithms. Clearly, the metadata needs to be transformed after each image processing operation, so that it reflects the changes made to the image. Although stochastic process theory provides the background for generating such transformations, the theory has not been applied in a digital imaging system to derive needed input data for image transformations.